Automatic recovery of the left ventricular blood pool in cardiac cine magnetic resonance images

ABSTRACT

An exemplary embodiment of the present invention includes a method of detecting a left ventricle blood pool. The method includes: localizing a region of interest (ROI) in a three-dimensional temporal (3D+T) image based on motion information of each slice of the 3D image over time, thresholding the ROI to determine pixels of the ROI that correspond to blood, extracting connected components from the determined pixels, clustering the extracted connected components into groups based on criteria that are indicative of a blood pool of a left ventricle, and selecting one of the groups as the left ventricle blood pool.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.61/035,887, filed on Mar. 12, 2008, the disclosure of which isincorporated by reference herein.

BACKGROUND OF THE INVENTION

1. Technical Field

The present disclosure relates to medical imaging of the heart, and moreparticularly, to detection of the left ventricular blood pool in theheart.

2. Discussion of Related Art

Cardiovascular disease has become the largest cause of death in themodern world and is an important health concern. Imaging technologiessuch as magnetic resonance (MR) imaging allow physicians tonon-invasively observe the behavior of the heart. Physicians areparticularly interested in the left ventricle (LV) because it pumpsoxygenated blood out to the rest of the body. To diagnose medicalconditions, it is useful to be able to quantify the volume of blood poolof the LV over time and estimate its ejection fraction, cardiac output,peak ejection rate, filling rate, myocardial thickening, etc. Thesequantities can be computed once the LV is outlined in several images ofthe heart. Manual outlining of the LV is very cumbersome however, andmany physicians can only manually outline the end-diastolic (ED) andend-systolic (ES) phases. While the ejection fraction and cardiac outputcan be computed from the resulting outlined LVs, they do not provideenough information to estimate peak ejection rate or filling rate.

Accordingly, it would be beneficial to be able to provide a system thatautomatically segments images of the heart to locate the LV. As a firststep it is important to localize the LV blood pool. Then, the localizedblood pool can be used to initialize more elaborate LV segmentationmethods. However, it can be difficult to localize the LV blood poolbecause MR intensities are not consistent across acquisitions and bloodpixels cannot easily be identified in the images. Further, manyacquisitions cover slices beyond the LV itself to guarantee it is seenin all phases. This means that some slices can be below the apex andcontain no blood pool, and some slices can be above the mitral valve andcontain the left atrium blood pool.

Some researchers have constructed models to aid in LV segmentation. Oneconventional method uses a 4D probabilistic atlas of the heart and a 3Dintensity template to register an ED frame to localize the left andright ventricles. A second conventional method uses a hybrid activeshape and appearance model to locate the heart using a Hough transform.However, both of these methods are too slow for clinical practice.Further, models have difficulty capturing variability outside theirtraining sets. For example, pathological cases that fall outside thestandard set of shapes may not be recognized and appearance models mayneed to be re-trained for new acquisition protocols and sequences.

One conventional modeling method is fast enough for clinical practice,but still depends on a learned appearance represented by a Markov chain.Another conventional modeling method combines a statistical model withcoupled mesh surfaces. However, many of the datasets tested using thestatistical modeling method exhibit breathing artifacts andthrough-plane motion. Further, the statistical modeling method assumesthat the heart is located in the center of an image, which is rarely avalid assumption.

Other conventional methods use simple image processing techniques to aidin LV segmentation. One conventional image processing method operates onthe assumption that the coverage of a short-axis image stack stops atthe mitral valve and does not go into the atrium. However, thisassumption is not reasonable since physicians tend to increase thecoverage of the image stack to correct for potential motion after theacquisition of the localized images to make sure that the LV is visibleduring all phases. Further, when the top slice extends into the atrium,the method can not separate the LV from the right ventricle (RV) withoutuser intervention at the mitral valve.

Thus, there is a need for more efficient methods of automaticallydetecting the LV blood pool.

SUMMARY OF THE INVENTION

An exemplary embodiment of the present invention includes a method ofdetecting a left ventricle blood pool. The method includes: localizing aregion of interest (ROI) in a three-dimensional temporal (3D+T) imagebased on motion information of each slice of the 3D image over time,thresholding the ROI to determine pixels of the ROI that correspond toblood, extracting connected components from the determined pixels,clustering the extracted connected components into groups based oncriteria that are indicative of a blood pool of a left ventricle, andselecting one of the groups as the left ventricle blood pool.

The 3D image may be, for example, an MR image. The extracted connectedcomponents may be three (2D+T) dimensional. The identifying may include:ranking the groups in decreasing order of size, examining at least thefirst two group, and selecting a group from the at least two groups thathas a highest confidence for indicating the left ventricle blood pool.The pixels may be indicative of blood when their intensities are above athreshold intensity. The grouping may include: associating each 2D+Tconnected component with a vertex in a graph and defining edges betweenthe vertices of neighboring slices, calculating first measures for eachvertex that are indicative of an LV blood pool, calculating secondmeasures for each pair of vertices in neighboring slices that areindicative of the similarity between the connected components, andclustering the 2D connected components into the groups based on thefirst and second measures.

The clustering of the 2D+T connected components into the groups based onthe first and second measures may include: generating a confidence valuefor each vertex from the first measures, defining an edge weight foreach edge based on the confidence value and the second measures, andclustering the 2D connected components into the groups based on the edgeweights.

The first measures may include a shrinking measure that indicates theamount that a 2D connected component contracts over time, a roundnessmeasure that indicates an average roundness of a 2D connected componentover time, a connectedness measure that indicates the degree ofconnectedness of a 2D connected component over time, and a concavitymeasure that indicates the degree of concavity of a 2D connectedcomponent over time.

The second measures include an overlap measure that indicates the degreeof overlap between two 2D connected components, a distance measure thatindicates the distance between centers of two 2D connected components,and a size measure that indicates a difference in size between areas oftwo 2D connected components. The ROI may be generated by computing thefirst harmonic of a Fourier transform over time for each slice of the 3Dimage to generate harmonic images to highlight moving areas, where theROI includes a part of the moving areas. The generation of the ROI mayinclude: fitting a 3D line through 2D centroids of the harmonic imagesto remove distant artifacts, and discarding connected components of themoving image with low confidences for indicating the heart. Theconfidences may be based on relative motions and relative sizes of theconnected components.

An exemplary embodiment of the present invention includes a method ofdetecting a blood pool of a left ventricle. The method includes:thresholding a region of interest (ROI) to determine pixels of the ROIthat correspond to blood, extracting connected components from thedetermined pixels, clustering the extracted connected components intogroups based on criteria that are indicative of a blood pool of a leftventricle, and selecting one of the groups as the left ventricle bloodpool.

The clustering may include: generating a first confidence value for eachconnected component that is indicative of a left ventricle blood pool,generating a second confidence value for pairs of the connectedcomponents that is indicative of the similarity between the connectedcomponents, and clustering the connected components into groups based onthe first and second confidence values.

The first confidence value may be derived from a shrinking measure thatindicates the amount that a connected component contracts, a roundnessmeasure that indicates an average roundness of connected component, aconnectedness measure that indicates the degree of connectedness of aconnected component, and a concavity measure that indicates the degreeof concavity of a connected component. The second confidence value maybe derived from an overlap measure that indicates the degree of overlapbetween a pair of connected components, a distance measure thatindicates the distance between centers of a pair of connectedcomponents, and a size measure that indicates a difference in sizebetween areas of a pair of connected components.

An exemplary embodiment of the present invention includes a system fordetecting a left ventricle blood pool. The system includes alocalization unit, a thresholding unit, an extracting unit, and aclustering unit. The localization unit locates a region of interest(ROI) in a three-dimensional (3D) image based on motion information ofeach slice of a 3D image over time. The thresholding unit performs athresholding on the ROI to determine pixels of the ROI that correspondto blood. The extracting unit extracts connected components from thedetermined pixels. The clustering unit divides the extracted connectedcomponents into groups based on criteria that are indicative of a bloodpool of a left ventricle.

The localization unit may determine the motion information by performinga Fourier transform on each slice of the 3D image over time. Theclustering unit may divide the extracted connected components intogroups using isoperimetric clustering. Parameters of the isoperimetricclustering may include measures for each connected component and pairsof the connected components that are indicative of a blood pool of aleft ventricle of a heart and the similarity between connectedcomponents.

An exemplary embodiment of the present invention includes a method ofsegmenting a region of interest (ROI) in a 3D+T image of a heart. Themethod includes: determining connected components in the ROI that onlyinclude blood pixels of the 3D image, clustering the connectedcomponents between slices of the ROI into groups using isoperimetricclustering, and selecting one of the groups as the left ventricle bloodpool. Parameters of the isometric clustering may include an amount thateach connected component shrinks over time and an average roundness ofeach connected component over time. The selecting one of the groups mayinclude: ranking the groups in decreasing order of size, examining atleast the first two groups, and selecting a group from the at least twogroups that has a highest confidence for indicating the left ventricleblood pool.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the invention can be understood in more detailfrom the following descriptions taken in conjunction with theaccompanying drawings in which:

FIG. 1 illustrates a high-level flow-chart of a method of detecting ablood pool of a left ventricle, according to an exemplary embodiment ofthe present invention;

FIG. 2 illustrates a high-level flow chart of a method for locating theheart in an image, according to exemplary embodiment of the presentinvention;

FIG. 3 illustrates an exemplary graph that may be constructed when themethod of FIG. 1 is performed;

FIG. 4 illustrates a system for detecting a left ventricle blood pool,according to an exemplary embodiment of the present invention; and

FIG. 5 shows an example of a computer system capable of implementing themethods and systems according to embodiments of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

In general, exemplary embodiments for systems and methods of detecting ablood pool of a left ventricle from a plurality of images will now bediscussed in further detail with reference to FIGS. 1-5. This inventionmay, however, be embodied in different forms and should not be construedas limited to the embodiments set forth herein. Rather, theseembodiments are provided so that this disclosure will be thorough andcomplete, and will fully convey the scope of the invention to thoseskilled in the art.

It is to be understood that the systems and methods described herein maybe implemented in various forms of hardware, software, firmware, specialpurpose processors, or a combination thereof. In particular, at least aportion of the present invention may be implemented as an applicationcomprising program instructions that are tangibly embodied on one ormore program storage devices (e.g., hard disk, magnetic floppy disk,RAM, ROM, CD ROM, etc.) and executable by any device or machinecomprising suitable architecture, such as a general purpose digitalcomputer having a processor, memory, and input/output interfaces. It isto be further understood that, because some of the constituent systemcomponents and process steps depicted in the accompanying Figures may beimplemented in software, the connections between system modules (or thelogic flow of method steps) may differ depending upon the manner inwhich the present invention is programmed. Given the teachings herein,one of ordinary skill in the related art will be able to contemplatethese and similar implementations of the present invention.

FIG. 1 illustrates a high-level flow chart of a method of detecting ablood pool of a left ventricle from a plurality of images, according toan exemplary embodiment of the present invention. Referring to FIG. 1,the method includes: localizing a heart in a 3D+T image based on motioninformation of each slice of the 3D image over time to generate a regionof interest (ROI) (S101), determining pixels of the ROI that areindicative of blood (S102), extracting connected components from thedetermined pixels (S103), grouping the connected components fromneighboring slices of the 3D image based on criteria that are indicativeof a blood pool of a left ventricle (S104), and identifying the groupthat corresponds to the LV blood pool (S105).

The step (S101) of localizing the heart may be performed by the methodof FIG. 2, according to an exemplary embodiment of the presentinvention. Referring to FIG. 2, the first harmonic of the Fouriertransform over time for each slice of the 3D image is computed togenerate a plurality of 3D harmonic images H₁ ^(s)(x,y) (S201). Theharmonic images highlight the moving structures (e.g. cardiac chamber,large vessels, etc.) within the 3D image.

Next a 3D line is fitted through the 2D centroids of the harmonic images(S202). Distances between points of the harmonic images may be weighted,histogrammed, and thresholded to remove the farthest points (S203). Thisprocedure is repeated until the 3D centroid of the harmonic imagesbecomes stable (S204).

Then for each slice, an average response H₁ ^(s) of the first harmonicis computed and each harmonic image is thresholded at 2H₁ ^(s) to retainonly the strongly moving areas (S205). Connected components (CC) areextracted and then grouped between slices to generate regions ofinterest that are consistent in space (S206). The CC with the largestaverage motion (e.g., over all pixels) is identified in each slice andthe relative motion of the other CCs is computed. The CC with thelargest size is identified in each slice and the relative sizes of theother CCs are computed. A confidence of a CC may then be defined as itsrelative size times its relative motion (S207). The CCs with thesmallest confidences are removed one at a time (S208) and this processis repeated until a slice containing a single CC (denoted Ĉ) isidentified (S209). In the other slices, a 2D overlap between each CC andĈ is computed and the confidence becomes the relative size times thisoverlap (S210). Connected components with a confidence lower than athreshold value (e.g., 0.1) are discarded (S211). The ROI may be definedas the convex hull of the retained CCs in each of the slices (S212).

The step (S102) of determining pixels of the ROI indicative of blood mayinclude performing a thresholding on the ROI. For example, thethresholding may be performed using Otsu's method to distinguish thebright pixels (i.e., those more likely to represent blood) from therest. The bright pixels may also be distinguished by retaining thosehaving intensities that are above a predefined intensity threshold anddiscarding the rest. The bright pixels may then be grouped intoconnected components.

In the step (103) of extracting the connected components, twodimensional connected components may be extracted from the ROI over time(hereinafter referred to as “2D+T connected components”). The 2D+Tconnected components may be extracted instead of 3D+T connectedcomponents, especially when the first slices of the datasets usedcontain the left atrium (LA). For example, the bright pixels of the LVwould have been connected above the bright pixels of the LA, which wouldhave been connected below to the bright pixels of the RV, and the LV andRV would have been connected. With 2D+T connected components, the LV andthe RV are connected only in the mitral valve slice. The connectedcomponents are likely to include the LV blood pool, the RV blood pool,the aorta, and other noisy regions.

In the step (S104) of grouping the connected components from neighboringslices, the grouping may be performed using an isoperimetric clusteringmethod. The isoperimetric clustering method partitions a weighted graphby minimizing the perimeter to area ratio

$\left( {{e.g.},{{{the}\mspace{14mu}{isoperimetric}\mspace{14mu}{ratio}\mspace{14mu}{h(S)}} = \frac{{\partial S}}{S}}} \right).$A graph is a pair G=(V, E) with vertices vεV and edges eεE⊂V×V. An edgebetween two vertices v_(i) and v_(j) is denoted e_(ij). In oneembodiment of the present invention, the largest four connectedcomponents in each slice are each associated with a vertex in the graphand edges are defined between vertices in neighboring slices. However,the present invention is not limited to the largest four connectedcomponents, as other embodiments can make use of a lesser or greaternumber of connected components.

The parameter A_(p)(v_(i)) is used to represent the area in phase p(e.g., of the heart) of the connected component associated with a vertexv_(i). Parameters A^(mt)(v_(i)) and A^(M)(v_(i)) are to used torespectively represent the minimum and maximum areas over time. Measuresor parameters that are indicative of the characteristics of an LV bloodpool may be computed for each connected component.

A shrinking measure S(v_(i)) that indicates the amount that an object(e.g., a connected component) contracts over time may be represented byequation 1 as follows:

$\begin{matrix}{{S\left( v_{i} \right)} = {\frac{A^{m}\left( v_{i} \right)}{A^{M}\left( v_{i} \right)}.}} & (1)\end{matrix}$A roundness measure R(v_(i)) indicates the degree of roundness exhibitedby an object over time. The roundness measure R(v_(i)) may be computedfrom the ratio of the smallest eigenvalue to the largest eigenvalue of aconnected component in each phase, averaged over time. A connectednessmeasure C(v_(i)) indicates the degree of connectedness exhibited by anobject over time. Connected components when observed in each phase canbe composed of multiple pieces. The relative size of these pieces isdenoted by r_(p) ^(j)(v_(i)), j=1, . . . ,n_(i) and the connectednessmeasure C(v_(i)) may be computed by equation 2 as follows:

$\begin{matrix}{{C\left( v_{i} \right)} = {\frac{1}{P}{\sum\limits_{p = 1}^{P}{\sum\limits_{j = 1}^{ni}{{r_{p}^{j}\left( v_{i} \right)}.}}}}} & (2)\end{matrix}$A concavity measure D(v_(i)) indicates the degree of concavity exhibitedby an object over time. The concavity measure D(v_(i)) may be computedby determining the maximum distance (e.g., normalized between 0 and 1)between the object and its convex hull, averaged over time.

An overall confidence L(v_(i)) of a connected component may becalculated using some or all of the above measures. For example, in oneembodiment of the present invention, the overall confidence L(v_(i)) iscomputed by equation 3 as follows:L(v _(i))=F ₁ S(v _(i))(1−R(v _(i)))C(v _(i))^(F) ² (1−D(v _(i)))^(F) ³,  (3)where factors F₁, F₂, F₃ may be determined empirically. In oneembodiment, factor F₁= 1/50 and factors F₂ and F₃=10. However, thepresent invention is not limited to these particular factors, as thefactors may be adjusted in various ways. Further, the overall confidenceL(v_(i)) may be computed using a confidence equation that varies fromequation 3 or by an equation that uses less than the above measuresS(v_(i)), R(v_(i)), C(v_(i)), and D(v_(i)).

Edge weights, denoted w(e_(ij)) may indicate a similarity betweenvertices. The normalized area a_(p)(v_(i)) of a connected component inphase p may be computed by equation 4 as follows:

$\begin{matrix}{{a_{p}\left( v_{i} \right)} = {\frac{{A_{p}\left( v_{i} \right)} - {A^{m}\left( v_{i} \right)}}{{A^{M}\left( v_{i} \right)} - {A^{m}\left( v_{i} \right)}}.}} & (4)\end{matrix}$Measures or parameters that are indicative of the characteristics of anLV blood pool may be computed for each pair of connected components inneighboring slices.

An overlap measure O(v_(i), v_(j)) indicates the degree of overlapbetween two 2D+T connected components. The overlap measure O(v_(i),v_(j)) may be computed by determining the intersection of the 2D+Tconnected components divided by their union. For example, in oneexemplary embodiment, if the overlap measure O(v_(i), v_(j))<0.001, theedge e_(ij) is discarded. A distance measure D(v_(i), v_(j)) indicatesthe distance between the centers of connected components averaged overtime. A resemblance measure T(v_(i), v_(j)) indicates the resemblance ofthe area-time curves of a pair of connected components. The resemblancemeasure T(v_(i), v_(j)) may be computed using equation 5 as follows:

$\begin{matrix}{{T\left( {v_{i},v_{j}} \right)} = {\frac{1}{P}{\sum\limits_{p}^{\;}{{{{a_{p}\left( v_{i} \right)} - {a_{p}\left( v_{j} \right)}}}.}}}} & (5)\end{matrix}$A size measure S(v_(i), v_(j)) indicates a difference in size between apair of connected components. The size measure S(v_(i), v_(j)) may becomputed using equation 6 as follows:

$\begin{matrix}{{{S\left( {v_{i},v_{j}} \right)} = {\frac{1}{P}\left( {\sum\limits_{p}^{\;}{\max\left( {1,\frac{A_{p}\left( v_{i} \right)}{A_{p}\left( v_{j} \right)}} \right)}} \right)}},} & (6)\end{matrix}$where v_(i) is on the slice below v_(j). The connected components maygrow smaller as one traverses down through slices closer to the apex.Thus, the size measure S(v_(i), v_(j)) may stay close to one forconnected components in the LV blood pool.

An edge cost c(v_(i), v_(j)) may be computed using equation 7 asfollows:

$\begin{matrix}{{c\left( {v_{i},v_{j}} \right)} = {\frac{{D\left( {v_{i},v_{j}} \right)}{T\left( {v_{i},v_{j}} \right)}{S\left( {v_{i},v_{j}} \right)}}{O\left( {v_{i},v_{j}} \right)}{L\left( v_{i} \right)}{{L\left( v_{j} \right)}.}}} & (7)\end{matrix}$An edge weight w(e_(ij)) may be computed from the edge costc(v_(i),v_(j)). For example, the edge weight w(e_(ij)) may be set equalto 1/c(v_(i), v_(j)).

The isoperimetric clustering method uses an indicator vector x, aperimeter, an area, and a Laplacian matrix. The indicator vector x takesa binary value at each vertex and encodes the partition S according theequation 8 as follows:

$\begin{matrix}{x_{i\;}\left\{ \begin{matrix}0 & {{{{if}\mspace{14mu} v_{i}} \in S},} \\1 & {{{if}\mspace{14mu} v_{i}} \in {\overset{\_}{S}.}}\end{matrix} \right.} & (8)\end{matrix}$The perimeter and area of the partition may be respectively defined byequations 9 and 10 as follows:|∂S|=x ^(T) L _(x,)  (9)|S|=x ^(T)1,  (10)where 1 is the unit vector and L is the Laplacian matrix, which isdefined by equation 11 as follows:

$\begin{matrix}{L_{ij} = \left\{ {{\begin{matrix}{{{d_{i}\mspace{14mu}{if}\mspace{14mu} i} = j},} \\{{{{- {w\left( e_{ij} \right)}}\mspace{14mu}{if}\mspace{14mu} e_{ij}} \in E},} \\{{0\mspace{14mu}{otherwise}},{and}}\end{matrix}d_{i}} = {\sum\limits_{{eij}\;}^{\;}{{w\left( e_{ij} \right)}.}}} \right.} & (11)\end{matrix}$

The indicator vector x may be recovered by solving the linear systemLx=1, which results in a real-valued solution for x. This can beconverted to a binary partition by ranking the x_(i)'s and choosing thethreshold that yields the minimum value for the isoperimetric ratioh(S). The ground node may be chosen as the center of the graph (e.g.,the vertex for which the shortest path to the farthest vertex thesmallest, which may be recovered using Floyd Warhsall's method). Thisground node may perform better to recover elongated clusters rather thanthe node with the largest degree. The graph is recursively partitioneduntil the isoperimetric ratio of the sub-partitions is larger than astopping criteria.

A grouping obtained using the above methods is likely to provide a bloodpool cluster that is reasonably large, has connected components thatshrink and expand over time, is reasonably round, and contain at leastone main piece in each phase. The clusters may be ranked in decreasingorder of size. In one embodiment, at least two of the largest clustersare examined, as well as those as large as the first two and any clusterof size larger than three. This is merely an example, as a fewer orgreater number of the ordered clusters may be examined, and the clustersmay be sized larger or smaller than three. Once the clusters have beenidentified, a single one of the clusters {circumflex over (K)} is chosensuch that a function is minimized. The function may be embodied byequation 12 as follows:

$\begin{matrix}{\frac{1}{N_{K}^{2}}{\sum\limits_{v_{i \in k}}^{\;}{L^{\prime}\left( v_{i} \right)}}} & (12)\end{matrix}$where N_(k) is the number of vertices in cluster K andL′(v_(i))=S(v_(i))(1−R(v_(i)))C(v_(i))¹⁰ is the confidence of aconnected component. The cluster with smaller L′ is the blood pool. The2D convex hulls of the connected components in {circumflex over (K)}define the left ventricle blood pool. Concavity D may be included inalternate embodiments of L′.

FIG. 3 illustrates an example of a graph 300 that may be constructedwith edge weights w(e_(ij)) and confidences L′(v_(i)) using the abovedescribed isoperimetric clustering method and the above describedmeasures. The partitions are shown in the dashed lines and the LV bloodpool partition is shown in the dotted lines. The numbers in each node(vertex) of the graph 300 are the confidences L′(v_(i)) and the numberson each edge between the vertices are the edge weights w(e_(ij)). Forexample, the LV blood pool is bounded by the dotted lines and includesvertices having values of 8, 7, 7, 4, 4, and 4. Each column of verticescorresponds to a different slice. The vertices within a given row of thegraph 300 correspond to each of the different connected components.

FIG. 4 illustrates a system 400 for detecting a left ventricle bloodpool. The system includes: a localization unit 401, a thresholding unit402, an extracting unit 403, and a clustering unit 404. The localizationunit 401 is configured to locate a region of interest (ROI) in athree-dimensional (3D) image based on motion information of each sliceof a 3D image over time. The thresholding unit 402 is configured toperform a thresholding on the ROI to determine pixels of the ROI thatcorrespond to blood. The extracting unit 403 is configured to extractconnected components from the determined pixels. The clustering unit 404is configured to divide the extracted connected components into groupsbased on criteria that are indicative of a blood pool of a leftventricle. The localization unit 401 may determine the motioninformation by performing a Fourier transform on each slice of the 3Dimage over time. The clustering unit 404 may divide the extractedconnected components into groups using isoperimetric clustering.Parameters of the isoperimetric clustering may include measures for eachconnected component and pairs of the connected components that areindicative of a blood pool of a left ventricle of a heart.

FIG. 5 shows an example of a computer system which may implement amethod and system of the present disclosure. The system and method ofthe present disclosure may be implemented in the form of a softwareapplication running on a computer system, for example, a mainframe,personal computer (PC), handheld computer, server, etc. The softwareapplication may be stored on a recording media locally accessible by thecomputer system and accessible via a hard wired or wireless connectionto a network, for example, a local area network, or the Internet.

The computer system referred to generally as system 1000 may include,for example, a central processing unit (CPU) 1001, random access memory(RAM) 1004, a printer interface 1010, a display unit 1011, a local areanetwork (LAN) data transmission controller 1005, a LAN interface 1006, anetwork controller 1003, an internal bus 1002, and one or more inputdevices 1009, for example, a keyboard, mouse etc. As shown, the system1000 may be connected to a data storage device, for example, a harddisk, 1008 via a link 1007.

Although the illustrative embodiments have been described herein withreference to the accompanying drawings, it is to be understood that thepresent invention is not limited to those precise embodiments, and thatvarious other changes and modifications may be affected therein by oneof ordinary skill in the related art without departing from the scope orspirit of the invention. All such changes and modifications are intendedto be included within the scope of the invention.

What is claimed is:
 1. A method of detecting a blood pool of a leftventricle (LV) in a three-dimensional temporal (3D+T) image, the methodcomprising: localizing a heart in a moving 3D image based on motioninformation of each slice of each 3D image of the moving 3D image over aperiod of time to generate a region of interest (ROI); determiningpixels of the ROI that are indicative of blood; extracting firstconnected components from the determined pixels; grouping the firstconnected components from neighboring slices of the 3D image based oncriteria that are indicative of a blood pool of a left ventricle; andidentifying the group that corresponds to the LV blood pool wherein thelocalizing of the heart comprises: identifying a single connectedcomponent from second connected components among the slices based onrelative sizes and relative motions among the second connectedcomponents; and identifying a subset of the second connected componentsbased on the relative sizes and overlaps between the single connectedcomponent and the other second connected components.
 2. The method ofclaim 1, wherein the connected components are two dimensional componentsthat move over time (2D+T).
 3. The method of claim 1, wherein the pixelsare indicative of blood when their intensities are above a thresholdintensity.
 4. The method of claim 1, wherein the grouping comprises:associating each first connected component with a vertex in a graph anddefining edges between the vertices of neighboring slices; calculatingfirst measures for each vertex that are indicative of an LV blood pool;calculating second measures for each pair of vertices in neighboringslices that are indicative of the similarity between the first connectedcomponents; and clustering the first connected components into thegroups based on the first and second measures.
 5. The method of claim 4,wherein clustering the first connected components into the groups basedon the first and second measures comprises: generating a confidencevalue for each vertex from the first measures; defining an edge weightfor each edge based on the confidence value and the second measures; andclustering the first connected components into the groups based on theconfidence values and the edge weights.
 6. The method of claim 5,wherein the first measures include a shrinking measure that indicatesthe amount that a first connected component contracts over time, aroundness measure that indicates an average roundness of a firstconnected component over time, a connectedness measure that indicatesthe degree of connectedness of a first connected component over time,and a concavity measure that indicates the degree of concavity of afirst connected component over time.
 7. The method of claim 5, whereinthe second measures include an overlap measure that indicates the degreeof overlap between a pair of the first connected components, a distancemeasure that indicates the distance between centers of a pair of thefirst connected components, and a size measure that indicates adifference in size between areas of the two first connected components.8. The method of claim 1, wherein the ROI is generated by computing thefirst harmonic of a Fourier transform over time for each slice of the 3Dimage to generate harmonic images to highlight moving areas, wherein theROI includes a part of the moving areas.
 9. The method of claim 8,wherein the generation of the ROI includes: fitting a 3D line through 2Dcentroids of the harmonic images to remove distant artifacts.
 10. Themethod of claim 1, wherein the ROI is defined as a convex hull of thesub-set of second connected components.
 11. A method of detecting a leftventricle blood pool, the method comprising: identifying a singleconnected component from among a plurality of connected components basedon relative sizes and relative motions among the connected components;identifying a subset of the connected components based on the relativesizes and overlaps between the single connected component and the otherconnected components; generating a region of interest (ROI) from thesubset; thresholding the ROI to determine pixels of the ROI thatcorrespond to blood; extracting second connected components from thedetermined pixels; clustering the extracted second connected componentsinto groups based on criteria that are indicative of a blood pool of aleft ventricle; and selecting one of the groups as the left ventricleblood pool.
 12. The method of claim 11, wherein the clusteringcomprises: generating a first confidence value for each second connectedcomponent that is indicative of a left ventricle blood pool; generatinga second confidence value for pairs of the second connected componentsthat is indicative of the similarity between connected components; andclustering the second connected components into groups based on thefirst and second confidence values.
 13. The method of claim 12, whereinthe first confidence value is derived from a shrinking measure thatindicates the amount that a second connected component contracts, aroundness measure that indicates an average roundness of a secondconnected component, a connectedness measure that indicates the degreeof connectedness of a second connected component, and a concavitymeasure that indicates the degree of concavity of a second connectedcomponent.
 14. The method of claim 12, wherein the second confidencevalue is derived from an overlap measure that indicates the degree ofoverlap between a pair of the second connected components, a distancemeasure that indicates the distance between centers of a pair of thesecond connected components, and a size measure that indicates adifference in size between areas of a pair of the second connectedcomponents.
 15. The method of claim 11, wherein the ROI is defined as aconvex hull of the selected first connected components.
 16. A programstorage device readable by machine, tangibly embodying a program ofinstructions executable by the machine to perform method steps ofsegmenting a region of interest (ROI) in a 3D image of a heart themethod steps comprising: determining connected components in the ROIthat only include blood pixels of the 3D image; clustering the connectedcomponents between slices of the ROI into groups using isoperimetricclustering, wherein parameters of the isoperimetric clustering includean amount that each connected component shrinks over time and an averageroundness of each connected component over time, wherein the amountcorresponds to an area of the component at a first time divided by anarea of the component at a second later time; and selecting one of thegroups as the left ventricle blood pool.